Update PyCaret Anomaly detection Model

Question

I'm detecting anomalies in a time series data using pycaret. I'm taking in the data at every call, detecting and returning it. Everything is fine, but when coming to improving the performance, I'm planning to load the saved model, re-train it with less data(say daily instead of getting some 1000 days of data at once) and save the model again. Here its performance increases a lot, as it is training on only less data.

The problem is to update/re-train the model. I couldn't find any method to update the model.

Base Initially:
setup(dataframe)
model=createmodel(modelName)
results=assign_model(model)

What I'm trying to do.

try loading the model if already present.
setup(data_frame_new)
if model.exists:
    retrain_model
else:
    model=createmodel(modelName)
save_model(model)
results=assign_model(model)

So, now I have trained model and new data, how can I integrate both. Is there any way to retrain the model? I couldn't see any documentation on that so far. Or I might have overlooked. Please put forth your valuable comments to let me know how to achieve this.

Do you have any findings? I'm also stuck with retraining a trained model. — phantomhive, Aug 21 '22 at 11:55
@phantomhive Unfortunately, no. As of now (October 2022), as per the documentation, PyCaret doesn't support retraining a model yet. — panther_boy, Oct 05 '22 at 11:17

Update PyCaret Anomaly detection Model

0 Answers0